liuruoze/EasyPR
EasyPR
EasyPR is an automatic license plate recognition system designed to detect vehicle license plates and extract alphanumeric characters from images of Chinese vehicles. It functions as a deep learning OCR tool that converts image regions of license plates into machine-readable text strings.
The system includes a specialized detector for identifying vehicle plates within unconstrained environments and complex visual backgrounds. It also provides a synthetic data generator to create artificial image datasets used to train and improve the accuracy of the recognition models.
The project covers a multi-stage pipeline encompassing region-based plate detection, character segmentation, and alphanumeric classification. These capabilities are supported by supervised neural network training and convolutional feature extraction.
Features
- Chinese Plate Recognition - Identifies and extracts text from Chinese vehicle license plates in uncontrolled environments.
- License Plate Recognition - Transforms detected license plate image regions into machine-readable text strings for vehicle identification.
- Character Extraction - Extracts alphanumeric characters and colors from license plate images to convert them into digital text.
- Chinese Plate Detection - Identifies vehicle license plates from images captured in unconstrained environments for Chinese vehicles.
- Plate Detectors - Provides a detection model specialized in identifying vehicle plates within complex visual backgrounds.
- Region-based Detection - Locates the specific rectangular area of a license plate using region-based detection architectures.
- OCR - Utilizes a neural network based tool to convert image regions of license plates into machine-readable text.
- Computer Vision Training - Provides training routines for neural networks to improve the accuracy of character and plate detection.
- Synthetic Training Data Generators - Generates artificial training images with ground-truth annotations to improve model accuracy.
- Synthetic Dataset Generators - Includes a tool for creating artificial image datasets to train and improve plate recognition models.
- Convolutional Feature Extractors - Uses convolutional feature extractors to identify visual patterns and shapes within license plate images.
- Character Classification - Implements a classifier to assign text labels to cropped image segments of license plate characters.
- Vision Model Training - Provides capabilities for generating synthetic data and training neural networks for visual recognition.
- Neural Network Training - Optimizes neural network weights using labeled datasets of Chinese license plates to reduce recognition errors.
- Recognition Pipelines - Processes images through a multi-stage pipeline involving localization, character segmentation, and alphanumeric classification.
- Computer Vision Libraries - Flexible plate recognition for Chinese licenses.
Star history
How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.
